Abstract
Given the diverse focuses of emerging online social networks (OSNs), it is common that a user has signed up on multiple OSNs. Social hub services, a.k.a., social directory services, help each user manage and exhibit her OSN accounts on one webpage. In this work, we conduct a data-driven study by crawling over one million user profiles from about.me, a representative online social hub service. Our study aims at gaining insights on cross-OSN social influence from the crawled data. We first analyze the composition of the social hub users. For each user, we collect her social accounts from her social hub webpage, and aggregate the content generated by these accounts on different OSNs to gain a comprehensive view of this user. According to our analysis, there is a high probability that a user would provide consistent information on different OSNs. We then explore the correlation between user activities on different OSNs, based on which we propose a cross-OSN social influence prediction model. With the model, we can accurately predict a user’s social influence on emerging OSNs, such as Instagram, Foursquare, and Flickr, based on her data published on well-established OSNs like Twitter.
Similar content being viewed by others
References
Bakshy, E., Mason, W.A., Hofman, J.M., Watts, D.J.: Everyone is an influencer: quantifying influence on Twitter. In: Proc. of ACM WSDM (2011)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in Twitter: The million follower fallacy. In: Proc. of AAAI ICWSM (2010)
Cha, M., Benevenuto, F., Ahn, Y., Gummadi, P.K.: Delayed information cascades in Flickr: Measurement, analysis, and modeling. Comput. Netw. 56(3), 1066–1076 (2012)
Chen, T., Kaafar, M.A., et al.: Is more always merrier? A deep dive into online social footprints. In: Proc. of ACM WOSN (2012)
Chen, T., Guestrin, C.: XGBoost: A scalable tree boosting system. In: Proc. of ACM KDD (2016)
Cristofaro, E.D., Friedman, A., Jourjon, G., Kaafar, M.A., Shafiq, M.Z.: Paying for likes?: Understanding Facebook like fraud using honeypots. In: Proceedings of the 2014 Internet Measurement Conference, IMC 2014, Vancouver, BC, Canada, November 5-7, 2014, pp. 129–136 (2014)
Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: LIBLINEAR: A library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Farseev, A., Nie, L., Akbari, M., Chua, T.: Harvesting multiple sources for user profile learning: A big data study. In: Proc. of ACM ICMR (2015)
Fawcett, T.: An introduction to roc analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
Gabielkov, M., Rao, A., Legout, A.: Studying social networks at scale: Macroscopic anatomy of the Twitter social graph. In: Proc. of ACM SIGMETRICS (2014)
Gong, Q., Chen, Y., He, X., Zhuang, Z., Wang, T., Huang, H., Wang, X., Fu, X.: DeepScan: Exploiting deep learning for malicious account detection in location-based social networks. IEEE Communications Magazine (2018)
Gong, Q., Chen, Y., Hu, J., Cao, Q., Hui, P., Wang, X.: Understanding cross-site linking in online social networks to appear in ACM Transactions on the Web (2018)
Han, J., Choi, D., Chun, B.G., Kwon, T., Kim, H.C., Choi, Y.: Collecting, organizing, and sharing pins in Pinterest: Interest-driven or social-driven? In: Proc. of ACM SIGMETRICS (2014)
Hirsch, J.E.: An index to quantify an individual’s scientific research output. PNAS 102(46), 16,569–16,572 (2005)
Hu, Y., Manikonda, L., Kambhampati, S.: What we Instagram: A first analysis of Instagram photo content and user types. In: Proc. of AAAI ICWSM (2014)
Jain, P., Kumaraguru, P., Joshi, A.: Other times, other values: Leveraging attribute history to link user profiles across online social networks. Soc. Netw. Anal. Min. 6(1), 85 (2016)
Jang, J.Y., Han, K., Shih, P.C., Lee, D.: Generation like: Comparative characteristics in Instagram. In: Proc. of the ACM CHI, pp. 4039–4042 (2015)
Jin, L., Chen, Y., Wang, T., Hui, P., Vasilakos, A.V.: Understanding user behavior in online social networks: A survey. Commun. Mag. IEEE 51(9), 144–150 (2013)
Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proc. Natl. Acad. Sci. 110(15), 5802–5805 (2013)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proc of WWW (2010)
Li, J., Peng, W., Li, T., Sun, T., Li, Q., Xu, J.: Social network user influence sense-making and dynamics prediction. Expert Syst. Appl. 41(11), 5115–5124 (2014)
Liu, Q., Xiang, B., Yuan, N.J., Chen, E., Xiong, H., Zheng, Y., Yang, Y.: An influence propagation view of pagerank. ACM Trans. Knowl. Discov. Data 11 (3), 30:1–30:30 (2017)
Ma, J., Gao, W., Mitra, P., Kwon, S., Jansen, B.J., Wong, K., Cha, M.: Detecting rumors from microblogs with recurrent neural networks. In: Proc. of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI), pp. 3818–3824 (2016)
McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 153–157 (1947)
Noulas, A., Scellato, S., Mascolo, C., Pontil, M.: An empirical study of geographic user activity patterns in foursquare. In: Proc. of AAAI ICWSM (2011)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the Web Stanford InfoLab (1999)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Song, X., Chi, Y., Hino, K., Tseng, B.: Identifying opinion leaders in the blogosphere. In: Proc. of ACM CIKM (2007)
Suhara, Y., Xu, Y., Pentland, A.S.: DeepMood: Forecasting depressed mood based on self-reported histories via recurrent neural networks. In: Proc. of the 26th International Conference on World Wide Web (WWW), pp. 715–724 (2017)
Tang, J., Lou, T., Kleinberg, J.: Inferring social ties across heterogenous networks. In: Proc. of ACM WSDM (2012)
Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)
Vasconcelos, M.A., Ricci, S., et al.: Tips, dones and to-dos: Uncovering user profiles in foursquare. In: Proc. of ACM WSDM (2012)
Vasconcelos, M.A., Ricci, S.M.R., Almeida, J.M., Benevenuto, F., Almeida, V.A.F.: Tips, dones and todos: Uncovering user profiles in foursquare. In: Proc. of ACM WSDM (2012)
Venkatadri, G., Goga, O., Zhong, C., Viswanath, B., Gummadi, K.P., Sastry, N.: Strengthening weak identities through inter-domain trust transfer. In: Proc. of WWW (2016)
Wang, G., Konolige, T., Wilson, C., Wang, X., Zheng, H., Zhao, B.Y.: You are how you click: Clickstream analysis for Sybil detection. In: Proc. of USENIX Security (2013)
Weng, J., Lim, E.P., Jiang, J., He, Q.: TwitterRank: Finding topic-sensitive influential Twitterers. In: Proc. of ACM WSDM (2010)
Wilson, C., Boe, B., Sala, A., Puttaswamy, K.P., Zhao, B.Y.: User interactions in social networks and their implications. In: Proc. of ACM EuroSys (2009)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. In: Proc. of ICML (1997)
Yang, Z., Wilson, C., Wang, X., Gao, T., Zhao, B.Y., Dai, Y.: Uncovering social network Sybils in the wild. ACM Trans. Knowl. Discov. Data 8(1), 2:1–2:29 (2014)
Ye, S., Wu, S.F.: Measuring message propagation and social influence on Twitter.com. Int. J. Commun. Netw. Distrib. Syst. 11(1), 59–76 (2013)
Yuan, N.J., Zhang, F., Lian, D., Zheng, K., Yu, S., Xie, X.: We know how you live: Exploring the spectrum of urban lifestyles. In: Proc. of ACM COSN (2013)
Zhong, C., Chang, H.w., Karamshuk, D., Lee, D., Sastry, N.: Wearing many (social) hats: How different are your different social network personae? In: Proc. of AAAI ICWSM (2017)
Qingyuan, G, Yang, C, Xinlei, H, Fei, L, Xin, W, Yu, X, Xiaoming, F, Pan, H: Identification of Influential Users in Emerging Online Social Networks Using Cross-Site Linking. In: Proc. of ChineseCSCW (2018)
Acknowledgments
This work is sponsored by National Natural Science Foundation of China (No. 61602122, No. 71731004), Natural Science Foundation of Shanghai (No. 16ZR1402200), Shanghai Pujiang Program (No. 16PJ1400700).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article belongs to the Topical Collection: Special Issue on Social Computing and Big Data Applications
Guest Editors: Xiaoming Fu, Hong Huang, Gareth Tyson, Lu Zheng, and Gang Wang
Rights and permissions
About this article
Cite this article
Gong, Q., Chen, Y., Yu, X. et al. Exploring the power of social hub services. World Wide Web 22, 2825–2852 (2019). https://doi.org/10.1007/s11280-018-0633-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11280-018-0633-7